The present invention belongs to the field of processing residual stress, and discloses a method for calculating processing parameters for residual stress control by parameter inversion. This method comprises: (a) extracting a characteristic index reflecting the residual stress distribution characteristic from a residual stress distribution curve; (b) respectively presetting initial values of processing parameters for residual stress control, calculating an initial value of the characteristic index, and drawing curves of the characteristic index over the respective processing parameters to obtain respective fitted curves; (c) respectively establishing a relation formula between respective characteristic index increment of the processing parameters and the fitting curve; and (d) assigning the values and performing inversion calculation to obtain the required processing parameters. The present invention is simple in operation, reduces the number of tests, lowers the production cost, improves the processing residual stress distribution of the workpiece and improves the anti-fatigue life of the components.
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2. The method of claim 1, wherein the characteristic index includes the maximum surface residual stress, the maximum residual compressive stress depth in the surface layer or the depth of the surface tensile stress layer.
This invention relates to a method for analyzing and characterizing material properties, specifically focusing on surface residual stress in materials. The method addresses the need for precise measurement and evaluation of stress distributions in material surfaces, which is critical for assessing structural integrity, fatigue life, and performance in applications such as aerospace, automotive, and manufacturing. The method involves determining a characteristic index that quantifies key stress-related properties of a material's surface layer. This index includes the maximum surface residual stress, which indicates the highest stress present at the material's surface. Additionally, the index incorporates the maximum residual compressive stress depth, which measures how deep into the material the compressive stress extends, providing insight into its ability to resist cracking and deformation. The index also accounts for the depth of the surface tensile stress layer, which identifies the region where tensile stresses are present, a critical factor in material failure analysis. By evaluating these parameters, the method enables engineers to assess material performance under various conditions, optimize manufacturing processes, and predict long-term durability. The approach is particularly useful for materials subjected to cyclic loading, high temperatures, or corrosive environments, where stress distribution plays a significant role in material behavior. The method provides a standardized way to compare different materials or treatments, ensuring consistent and reliable evaluations.
3. The method of claim 1, wherein in the step (b), the processing parameters include cutting speed, feed rate, cutting depth, tool edge radius or tool rake angle.
This invention relates to a method for optimizing machining processes, particularly for improving the efficiency and precision of material removal in manufacturing. The method addresses the challenge of selecting appropriate processing parameters to enhance cutting performance while minimizing tool wear and surface defects. The core technique involves analyzing the relationship between multiple machining parameters and their impact on cutting efficiency, tool life, and workpiece quality. The method includes a step of determining processing parameters such as cutting speed, feed rate, cutting depth, tool edge radius, and tool rake angle. These parameters are adjusted based on real-time or pre-determined data to optimize the machining process. The tool edge radius and rake angle are critical factors that influence chip formation, cutting forces, and surface finish. By systematically varying these parameters, the method ensures that the cutting tool operates within optimal conditions, reducing the risk of premature wear or breakage. Additionally, the method may incorporate feedback mechanisms to dynamically adjust parameters during operation, further improving adaptability to different materials and cutting scenarios. The overall goal is to achieve higher material removal rates, longer tool life, and superior surface quality in machining operations.
4. The method of claim 1, wherein the initial value H(a10, a20, . . . , ai0, . . . , an0) of the characteristic index is calculated by a residual stress analytical model or experimentally measured.
5. The method of claim 1, wherein in the step (b), the drawn curves of the characteristic index over the respective processing parameters Ai are obtained by a processing residual stress theoretical model or experimental measurements.
This invention relates to a method for optimizing processing parameters in manufacturing processes, particularly for controlling material properties such as residual stress. The method addresses the challenge of accurately determining optimal processing parameters to achieve desired material characteristics, which is critical in industries like aerospace, automotive, and precision engineering where material performance directly impacts product reliability. The method involves analyzing the relationship between processing parameters and a characteristic index, such as residual stress, to identify optimal settings. The characteristic index is evaluated over a range of processing parameters, and the resulting curves are generated either through a theoretical model or experimental measurements. This allows for precise adjustments to processing conditions to achieve the desired material properties. The theoretical model or experimental data provides a reliable basis for predicting how changes in parameters like temperature, pressure, or time affect the material's residual stress, enabling more accurate and efficient process optimization. By using either a theoretical model or empirical measurements, the method ensures flexibility in application, accommodating scenarios where experimental data is limited or where theoretical models are more practical. This approach enhances process control, reduces trial-and-error iterations, and improves the consistency and quality of manufactured components. The method is particularly useful in high-precision applications where residual stress must be tightly controlled to prevent defects or failures.
6. The method of claim 2, wherein in the step (b), the processing parameters include cutting speed, feed rate, cutting depth, tool edge radius or tool rake angle.
This invention relates to a method for optimizing machining processes, particularly for controlling cutting tools in material removal operations. The method addresses the challenge of achieving precise and efficient material removal while minimizing tool wear and improving surface finish. The process involves adjusting processing parameters to enhance cutting performance. These parameters include cutting speed, feed rate, cutting depth, tool edge radius, and tool rake angle. By dynamically adjusting these variables, the method ensures optimal cutting conditions, reducing tool wear and improving the quality of the machined surface. The system may also incorporate feedback mechanisms to monitor and adjust parameters in real-time, ensuring consistent performance across different materials and cutting conditions. This approach is particularly useful in industries requiring high-precision machining, such as aerospace, automotive, and medical device manufacturing, where material integrity and surface finish are critical. The method provides a systematic way to balance cutting efficiency with tool longevity, addressing a long-standing need for adaptive machining solutions.
7. The method of claim 2, wherein the initial value H(a10, a20, . . . , ai0, . . . , an0) of the characteristic index is calculated by a residual stress analytical model or experimentally measured.
8. The method of claim 3, wherein the initial value H(a10, a20, . . . , ai0, . . . , an0) of the characteristic index is calculated by a residual stress analytical model or experimentally measured.
9. The method of claim 2, wherein in the step (b), the drawn curves of the characteristic index over the respective processing parameters Ai are obtained by a processing residual stress theoretical model or experimental measurements.
10. The method of claim 3, wherein in the step (b), the drawn curves of the characteristic index over the respective processing parameters Ai are obtained by a processing residual stress theoretical model or experimental measurements.
11. The method of claim 4, wherein in the step (b), the drawn curves of the characteristic index over the respective processing parameters Ai, are obtained by a processing residual stress theoretical model or experimental measurements.
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April 2, 2019
November 15, 2022
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